Improved tomato leaf disease classification through adaptive ensemble models with exponential moving average fusion and enhanced weighted gradient optimization Article Swipe
V. Pandiyaraju
,
A. M. Senthil Kumar
,
Praveen Joe I R
,
Shravan Venkatraman
,
Sarthak Kumar
,
S A Aravintakshan
,
A Abeshek
,
A. Kannan
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/fpls.2024.1382416
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/fpls.2024.1382416
Tomato is one of the most popular and most important food crops consumed globally. The quality and quantity of yield by tomato plants are affected by the impact made by various kinds of diseases. Therefore, it is essential to identify these diseases early so that it is possible to reduce the occurrences and effect of the diseases on tomato plants to improve the overall crop yield and to support the farmers. In the past, many research works have been carried out by applying the machine learning techniques to segment and classify the tomato leaf images. However, the existing machine learning-based classifiers are not able to detect the new types of diseases more accurately. On the other hand, deep learning-based classifiers with the support of swarm intelligence-based optimization techniques are able to enhance the classification accuracy, leading to the more effective and accurate detection of leaf diseases. This research paper proposes a new method for the accurate classification of tomato leaf diseases by harnessing the power of an ensemble model in a sample dataset of tomato plants, containing images pertaining to nine different types of leaf diseases. This research introduces an ensemble model with an exponential moving average function with temporal constraints and an enhanced weighted gradient optimizer that is integrated into fine-tuned Visual Geometry Group-16 (VGG-16) and Neural Architecture Search Network (NASNet) mobile training methods for providing improved learning and classification accuracy. The dataset used for the research consists of 10,000 tomato leaf images categorized into nine classes for training and validating the model and an additional 1,000 images reserved for testing the model. The results have been analyzed thoroughly and benchmarked with existing performance metrics, thus proving that the proposed approach gives better performance in terms of accuracy, loss, precision, recall, receiver operating characteristic curve, and F1-score with values of 98.7%, 4%, 97.9%, 98.6%, 99.97%, and 98.7%, respectively.
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- Type
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- Language
- en
- Landing Page
- https://doi.org/10.3389/fpls.2024.1382416
- https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2024.1382416/pdf
- OA Status
- gold
- Cited By
- 13
- References
- 63
- Related Works
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- OpenAlex ID
- https://openalex.org/W4397016754
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4397016754Canonical identifier for this work in OpenAlex
- DOI
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- Title
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Improved tomato leaf disease classification through adaptive ensemble models with exponential moving average fusion and enhanced weighted gradient optimizationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-05-17Full publication date if available
- Authors
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V. Pandiyaraju, A. M. Senthil Kumar, Praveen Joe I R, Shravan Venkatraman, Sarthak Kumar, S A Aravintakshan, A Abeshek, A. KannanList of authors in order
- Landing page
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https://doi.org/10.3389/fpls.2024.1382416Publisher landing page
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https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2024.1382416/pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2024.1382416/pdfDirect OA link when available
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Artificial intelligence, Artificial neural network, Computer science, Pattern recognition (psychology), Machine learning, MathematicsTop concepts (fields/topics) attached by OpenAlex
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13Total citation count in OpenAlex
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2025: 11, 2024: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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